import numpy as np
import pandas as pd
from itertools import cycle
from scipy import interp

# sklearn
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn import datasets
from sklearn.preprocessing import StandardScaler

# TensorFlow
import tensorflow as tf
from tensorflow.estimator import LinearClassifier

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## seaborn
import seaborn as sns
sns.set_context('paper', rc={'font.size':12,'axes.titlesize':14,'axes.labelsize':12})
sns.set_style('white')

## matplotlib
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
import matplotlib.gridspec as gridspec
import matplotlib.colors
from pylab import rcParams
plt.style.use('seaborn-whitegrid')
plt.rcParams['figure.figsize'] = 14, 8
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px
%config InlineBackend.figure_format = 'retina' 

import warnings
warnings.filterwarnings("ignore")

Tensorflow DNN Classifier

In this article, we demonstrate solving a classification problem in TensorFlow using Estimators using the UCI ML Wine recognition dataset. This dataset also can be accessed via the scikit-learn datasets.

Dataset Information:

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.

Data = datasets.load_wine(as_frame=True)
Target = 'Class'
Labels = [x.replace('_',' ').title() for x in Data['target_names']]
Data = pd.concat([Data['data'], pd.Series(Data['target']).to_frame(Target)], axis = 1)
Temp = ['Alcohol', 'Malic Acid', 'Ash', 'Alcalinity of Ash', 'Magnesium', 'Total Phenols', 'Flavanoids',
        'Nonflavanoid phenols', 'Proanthocyanins', 'Color Intensity', 'Hue', 'OD280/OD315 of Diluted Wines', 'Proline', 'Class']
Attributes = {}
for k, v in zip(Data.columns, Temp):
        Attributes[k] = v
del k, v, Temp
display(Data.rename(columns = Attributes).head())

	Alcohol	Malic Acid	Ash	Alcalinity of Ash	Magnesium	Total Phenols	Flavanoids	Nonflavanoid phenols	Proanthocyanins	Color Intensity	Hue	OD280/OD315 of Diluted Wines	Proline	Class
0	14.23	1.71	2.43	15.6	127.0	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065.0	0
1	13.20	1.78	2.14	11.2	100.0	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050.0	0
2	13.16	2.36	2.67	18.6	101.0	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185.0	0
3	14.37	1.95	2.50	16.8	113.0	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480.0	0
4	13.24	2.59	2.87	21.0	118.0	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735.0	0

def Data_info(Inp, Only_NaN = False):
    Out = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = Out.join(Inp.isnull().sum().to_frame(name = 'Number of NaN Values'), how='outer')
    Out ['Size'] = Inp.shape[0]
    Out['Percentage'] = np.round(100*(Out['Number of NaN Values']/Inp.shape[0]),2)
    Out.index.name = 'Features'
    if Only_NaN:
        Out = Out.loc[Out['Number of NaN Values']>0]
    return Out

Data_info(Data)

	Data Type	Number of NaN Values	Size	Percentage
Features
Class	int32	0	178	0.0
alcalinity_of_ash	float64	0	178	0.0
alcohol	float64	0	178	0.0
ash	float64	0	178	0.0
color_intensity	float64	0	178	0.0
flavanoids	float64	0	178	0.0
hue	float64	0	178	0.0
magnesium	float64	0	178	0.0
malic_acid	float64	0	178	0.0
nonflavanoid_phenols	float64	0	178	0.0
od280/od315_of_diluted_wines	float64	0	178	0.0
proanthocyanins	float64	0	178	0.0
proline	float64	0	178	0.0
total_phenols	float64	0	178	0.0

Data Correlations

Let's take a look at the variance of the features.

Fig, ax = plt.subplots(figsize=(17,12))
Temp = Data.drop(columns = [Target]).var().sort_values(ascending = False).to_frame(name= 'Variance').round(2).T
Temp.columns = [Attributes[x] for x in Temp.columns]
_ = sns.heatmap(Temp, ax=ax, annot=True, square=True,  cmap =sns.color_palette("OrRd", 20),
                  linewidths = 0.8, vmin=0, vmax=Temp.max(axis =1)[0],  annot_kws={"size": 12},
                  cbar_kws={'label': 'Feature Variance', "aspect":40, "shrink": .4, "orientation": "horizontal"})
lb = [x.replace(' ','\n').replace('\nof\n',' of\n') for x in [item.get_text() for item in ax.get_xticklabels()]]
_ = ax.set_xticklabels(lb)
_ = ax.set_yticklabels('')

Furthermore, we would like to standardize features by removing the mean and scaling to unit variance.

# Scaling
Temp = Data.drop(columns = Target).columns.tolist()
scaler = StandardScaler()
_ = scaler.fit(Data[Temp])
Data[Temp] = scaler.transform(Data[Temp])

# Variance Plot
Fig, ax = plt.subplots(figsize=(17,12))
Temp = Data.drop(columns = [Target]).var().sort_values(ascending = False).to_frame(name= 'Variance').round(2).T
Temp.columns = [Attributes[x] for x in Temp.columns]

_ = sns.heatmap(Temp, ax=ax, annot=True, square=True,  cmap =sns.color_palette('Greens'),
                  linewidths = 0.8, vmin=0, vmax=Temp.max(axis =1)[0], annot_kws={"size": 12},
                  cbar_kws={'label': 'Feature Variance', "aspect":40, "shrink": .4, "orientation": "horizontal"})
lb = [x.replace(' ','\n').replace('\nof\n',' of\n') for x in [item.get_text() for item in ax.get_xticklabels()]]
_ = ax.set_xticklabels(lb)
_ = ax.set_yticklabels('')

Train and Test sets

X = Data.copy()
y = Data.pop(Target)
Test_Size = 0.3

def Sets_Plot(Data, Test_Size):
    Temp = pd.DataFrame({'Set': ['Train', 'Test'],
             'Number of Instances':[int(Data.shape[0]*(1-Test_Size)), int(Data.shape[0]*Test_Size)]})
    Temp['Percentage'] = np.round(100* Temp['Number of Instances'].values /Temp['Number of Instances'].sum(), 2)
    fig = px.bar(Temp, y= ['',''], x= 'Number of Instances', orientation='h', color = 'Set', text = 'Percentage',
                 color_discrete_sequence = ['PaleGreen', 'LightBlue'], height = 180)
    fig.update_layout(plot_bgcolor= 'white', legend_orientation='h', legend=dict(x=0, y=1.7),
                      xaxis = dict(tickmode = 'array', tickvals = [0, Data.shape[0]], ticktext = ['','']))
    fig.update_traces(marker_line_color= 'Black', marker_line_width=1.5, opacity=1)
    fig.update_traces(texttemplate='%{text:.2}%      ', textposition='inside')
    fig.update_xaxes(title_text=None, range=[0, Data.shape[0]])
    fig.update_yaxes(title_text=None)
    fig.show()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= Test_Size, random_state=42)
display(pd.DataFrame(data={'Set':['X_train','X_test','y_train','y_test'],
                           'Shape':[X_train.shape, X_test.shape, y_train.shape, y_test.shape]}).set_index('Set').T)
Sets_Plot(Data, Test_Size)

Set	X_train	X_test	y_train	y_test
Shape	(124, 14)	(54, 14)	(124,)	(54,)

Input Function

The input function specifies how data is converted to a tf.data.Dataset that feeds the input pipeline in a streaming fashion. Moreover, an input function is a function that returns a tf.data.Dataset object which outputs the following two-element tuple:

• features - A Python dictionary in which:
  • Each key is the name of a feature.
  • Each value is an array containing all of that feature's values.
• label - An array containing the values of the label for every example.

def input_fn(features, labels, training=True, batch_size=256):
    """An input function for training or evaluating"""
    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))

    # Shuffle and repeat if you are in training mode.
    if training:
        dataset = dataset.shuffle(1000).repeat()
    
    return dataset.batch(batch_size)

Moreover, an estimator model consists of two main parts, feature columns, and a numeric vector. Feature columns provide explanations for the input numeric vector. The following function separates categorical and numerical columns (features)and returns a descriptive list of feature columns.

def Feat_Columns(Inp):
    Temp = Inp.dtypes.reset_index(drop = False)
    Temp.columns = ['Features', 'Data Type']
    Temp['Data Type'] = Temp['Data Type'].astype(str)
    # Numeric_Columns
    Numeric_Columns = Temp.loc[Temp['Data Type'].isin(['int64', 'int32', 'float64', 'float32']),'Features'].tolist()
    # Categorical_Columns
    Categorical_Columns = Temp.loc[Temp['Data Type'] == 'object','Features'].tolist()
        
    # Feature Columns
    feature_columns = []
    if len(Categorical_Columns)>0:
        for feature_name in Categorical_Columns:
          vocabulary = Inp[feature_name].unique()
          feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))
    if len(Numeric_Columns)>0:
        for feature_name in Numeric_Columns:
          feature_columns.append(tf.feature_column.numeric_column(feature_name))
    return feature_columns

my_feature_columns = Feat_Columns(X)

Estimator using the default optimizer

tf.keras.backend.clear_session()
IT = int(5e3)
classifier = tf.estimator.DNNClassifier(feature_columns=my_feature_columns,
                                        # Hidden layers.
                                        hidden_units= [100, 12, 3],
                                        # The model must choose between 3 classes.
                                        n_classes=len(Labels))
classifier.train(input_fn=lambda: input_fn(X_train, y_train, training=True), max_steps = IT)
result = classifier.evaluate(input_fn=lambda: input_fn(X_test, y_test, training=False))
clear_output()
display(pd.DataFrame(result, index = ['']).round(4))

	accuracy	average_loss	loss	global_step
	0.9259	0.4403	0.4403	5000

Predictions

pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])

ROC Curves

def ROC_Curve(y_test, probs, n_classes, FS = 7, ax = False, pad = 0.01):
    # converting y_test to categorical
    y_test_cat = tf.keras.utils.to_categorical(y_test, num_classes=n_classes, dtype='float32')
    # Compute ROC curve and ROC area for each class
    fpr = dict()
    tpr = dict()
    roc_auc = dict()
    for i in range(n_classes):
        fpr[i], tpr[i], _ = metrics.roc_curve(y_test_cat[:, i], probs[:, i])
        roc_auc[i] = metrics.auc(fpr[i], tpr[i])

    # Compute micro-average ROC curve and ROC area
    fpr["micro"], tpr["micro"], _ = metrics.roc_curve(y_test_cat.ravel(), probs.ravel())
    roc_auc["micro"] = metrics.auc(fpr["micro"], tpr["micro"])

    # First aggregate all false positive rates
    all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))

    # Then interpolate all ROC curves at this points
    mean_tpr = np.zeros_like(all_fpr)
    for i in range(n_classes):
        mean_tpr += interp(all_fpr, fpr[i], tpr[i])

    # Finally average it and compute AUC
    mean_tpr /= n_classes

    fpr["macro"] = all_fpr
    tpr["macro"] = mean_tpr
    roc_auc["macro"] = metrics.auc(fpr["macro"], tpr["macro"])

    # Plot all ROC curves
    if ax == False:
        fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(FS, FS))
        
    _ = ax.plot(fpr["micro"], tpr["micro"], label='micro-average ROC curve (area = {0:0.2f})'.format(roc_auc["micro"]),
                color='deeppink', linestyle=':', linewidth=4)
    _ = ax.plot(fpr["macro"], tpr["macro"], label='macro-average ROC curve (area = {0:0.2f})'.format(roc_auc["macro"]),
                color='navy', linestyle=':', linewidth=4)
    colors = cycle(['Aqua', 'DarkOrange', 'CornflowerBlue'])

    for i, color in zip(range(n_classes), colors):
        _ = ax.plot(fpr[i], tpr[i], color=color, lw=2, label='ROC curve of class {0} (area = {1:0.2f})'.format(i, roc_auc[i]))

    _ = ax.plot([0, 1], [0, 1], 'k--', lw=2)
    _ = ax.set_xlabel('False Positive Rate (FPR)')
    _ = ax.set_ylabel('True Positive Rate (TPR)')
    _ = ax.set_title('Receiver Operating Characteristic (ROC) Curves')
    _ = ax.legend(loc="lower right", fontsize = 12)
    _ = ax.set_xlim([-pad,1+pad])
    _ = ax.set_ylim([-pad,1+pad])
    # end
    
ROC_Curve(y_test, probs, n_classes = len(Labels), FS = 8)

Confusion Matrix

# Test set
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Confusion_Matrix = metrics.confusion_matrix(y_test, y_pred)

Results = pd.DataFrame(metrics.classification_report(y_test, y_pred, target_names=Labels, output_dict=True)).T
display(Results.round(2))

fig, ax = plt.subplots(1, 2, figsize=(14, 5))
fig.suptitle('Test Set', fontsize = 18)
_ = sns.heatmap(Confusion_Matrix, annot=True, annot_kws={"size": 14}, cmap="Blues", ax = ax[0],
                linewidths = 0.2, cbar_kws={"shrink": 1})
_ = ax[0].set_xlabel('Predicted labels')
_ = ax[0].set_ylabel('True labels'); 
_ = ax[0].set_title('Confusion Matrix');
_ = ax[0].xaxis.set_ticklabels(Labels)
_ = ax[0].yaxis.set_ticklabels(Labels)

Confusion_Matrix = Confusion_Matrix.astype('float') / Confusion_Matrix.sum(axis=1)[:, np.newaxis]
_ = sns.heatmap(Confusion_Matrix, annot=True, annot_kws={"size": 14}, cmap="Greens", ax = ax[1],
               linewidths = 0.2, vmin=0, vmax=1, cbar_kws={"shrink": 1})
_ = ax[1].set_xlabel('Predicted labels')
_ = ax[1].set_ylabel('True labels'); 
_ = ax[1].set_title('Normalized Confusion Matrix');
_ = ax[1].xaxis.set_ticklabels(Labels)
_ = ax[1].yaxis.set_ticklabels(Labels)

	precision	recall	f1-score	support
Class 0	0.90	1.00	0.95	19.00
Class 1	1.00	0.81	0.89	21.00
Class 2	0.88	1.00	0.93	14.00
accuracy	0.93	0.93	0.93	0.93
macro avg	0.93	0.94	0.93	54.00
weighted avg	0.93	0.93	0.92	54.00

Estimator using an optimizer with a learning rate decay

In this classification, the learning rate of your optimizer changes over time.

tf.keras.backend.clear_session()
classifier = tf.estimator.DNNClassifier(feature_columns=my_feature_columns,
            # Hidden layers.
            hidden_units= [1024, 512, 256],
            # The model must choose between 3 classes.
            n_classes=len(Labels),
            optimizer=lambda: tf.keras.optimizers.Adam(learning_rate=tf.compat.v1.train.exponential_decay(learning_rate=0.1,
            global_step=tf.compat.v1.train.get_global_step(), decay_steps=IT,decay_rate=0.96)))
#
classifier.train(input_fn=lambda: input_fn(X_train, y_train, training=True), max_steps= IT)
result = classifier.evaluate(input_fn=lambda: input_fn(X_test, y_test, training=False))
clear_output()
display(pd.DataFrame(result, index = ['']).round(4))

	accuracy	average_loss	loss	global_step
	0.963	347.404388	347.404388	5000

Predictions

pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
n_classes = len(Labels)
probs = np.array([pred['probabilities'] for pred in pred_dicts])

ROC Curves

ROC_Curve(y_test, probs, n_classes = len(Labels), FS = 8)

Confusion Matrix

# Test set
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Confusion_Matrix = metrics.confusion_matrix(y_test, y_pred)

Results = pd.DataFrame(metrics.classification_report(y_test, y_pred, target_names=Labels, output_dict=True)).T
display(Results.round(2))

fig, ax = plt.subplots(1, 2, figsize=(14, 5))
fig.suptitle('Test Set', fontsize = 18)
_ = sns.heatmap(Confusion_Matrix, annot=True, annot_kws={"size": 14}, cmap="Blues", ax = ax[0],
                linewidths = 0.2, cbar_kws={"shrink": 1})
_ = ax[0].set_xlabel('Predicted labels')
_ = ax[0].set_ylabel('True labels'); 
_ = ax[0].set_title('Confusion Matrix');
_ = ax[0].xaxis.set_ticklabels(Labels)
_ = ax[0].yaxis.set_ticklabels(Labels)

Confusion_Matrix = Confusion_Matrix.astype('float') / Confusion_Matrix.sum(axis=1)[:, np.newaxis]
_ = sns.heatmap(Confusion_Matrix, annot=True, annot_kws={"size": 14}, cmap="Greens", ax = ax[1],
               linewidths = 0.2, vmin=0, vmax=1, cbar_kws={"shrink": 1})
_ = ax[1].set_xlabel('Predicted labels')
_ = ax[1].set_ylabel('True labels'); 
_ = ax[1].set_title('Normalized Confusion Matrix');
_ = ax[1].xaxis.set_ticklabels(Labels)
_ = ax[1].yaxis.set_ticklabels(Labels)

	precision	recall	f1-score	support
Class 0	1.00	1.00	1.00	19.00
Class 1	1.00	0.90	0.95	21.00
Class 2	0.88	1.00	0.93	14.00
accuracy	0.96	0.96	0.96	0.96
macro avg	0.96	0.97	0.96	54.00
weighted avg	0.97	0.96	0.96	54.00

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References

1. Regression analysis Wikipedia page
2. Tensorflow tutorials
3. Online machine learning Wikipedia page
4. Learning rate Wikipedia page
5. S. Aeberhard, D. Coomans and O. de Vel, Comparison of Classifiers in High Dimensional Settings, Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland. (Also submitted to Technometrics).
6. S. Aeberhard, D. Coomans and O. de Vel, “THE CLASSIFICATION PERFORMANCE OF RDA” Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland. (Also submitted to Journal of Chemometrics).

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